Adaptive genetic algorithm with diversity-guided mutation and its global convergence property

Journal of Central South University of Technology (English Edition)

Volumn 11, Issue 3, 2004, Pages 323-327

  Li, Mei Yi ^a   Cai, Zi Xing ^a   Sun, Guo Rong ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Adaptive genetic algorithm; Diversity guided mutation; Global convergence; Markov chain

Indexed keywords

ADAPTIVE ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; MODAL ANALYSIS;

ADAPTIVE PROBABILITIES; DIVERSITY GUIDED MUTATION; GLOBAL CONVERGENCE; MARKOV CHAIN; MULTIMODAL FUNCTIONS; UNIMODAL FUNCTIONS;

GENETIC ALGORITHMS;

EID: 14744294474     PISSN: 10059784     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11771-004-0066-6     Document Type: Article

References (11)

1. Srinivas M, Patnai L M. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Trans Syst, Man, and Cybern, 1994, 24(4): 656-666.
2. ZONG Jing-qun. A kind of mixture adaptive genetic algorithm and analysis of property [J]. Systems Engineering: Theory and Practice, 2001 21(4): 14-18 (in Chinese).
3. ZHOU Ming, SUN Shu-dong. Genetic Algorithms: Theory and Applications [M]. Beijing: National Defence Industry Press, 1999. (in Chinese).
4. Rudolph G. Self-adaptive mutations may lead to premature convergence [J]. IEEE Transactions on Evolutionary Computation, 1998, 5(4): 410-414.
5. Burke E, Gustafson S, Kendall G. A survey and analysis of diversity measures in genetic programming [A]. The Genetic and Evolutionary Computation Conference 2002 [C]. New York, 2001.
6. Burke E, Gustafson S, Kendall G, et al. Advanced population diversity measures in genetic programming [A]. The Parallel Problem Solving from Nature Conference, 7th International Conference [C]. Granada, 2002.
7. Shimodaira H. A diversity control oriented genetic algorithm (DCGA): development and experimental results [A]. The Genetic and Evolutionary Computation Conference [C]. Florida, 1999.
8. Bosnian P A N, Thierens D. The balance between proximity and diversity in multi-objective evolutionary algorithms [J]. IEEE Transactions on Evolutionary Computation, 2003, 7(2): 174-188.
9. Iosifescu M. Finite Markov Processes and their Applications [M]. Chichester: Wiley, 1980.
10. Rudolph G. Convergence properties of canonical genetic algorithms [J]. IEEE Transactions on Neural Networks, 1994, 5(1): 96-101.
11. HE Lin, WANG Ke-jun, LI Guo-bin, et al. The discussion about the paper the analysis of global convergence and computational efficiency for genetic algorithm [J]. Control Theory and Applications, 2001, 18(1): 141-144. (in Chinese)